Computer Science > Computer Vision and Pattern Recognition

Title:Semi-supervised CNN for Single Image Rain Removal

Abstract: Single image rain removal is a typical inverse problem in computer vision.
The deep learning technique has been verified to be effective to this task and
achieved state-of-the-art performance. However, the method needs to pre-collect
a large set of image pairs with/without rains for training, which not only
makes the method laborsome to be practically implemented, but also tends to
make the trained network bias to the training samples while less generalized to
test samples with unseen rain types in training. To this issue, this paper
firstly proposes a semisupervised learning paradigm to this task. Different
from traditional deep learning methods which use only supervised image pairs
with/without rains, we put the real rainy images, without need of their clean
ones, into the network training process as well. This is realized by
elaborately formulating the residual between an input rainy image and its
expected network output (clear image without rains) as a concise patch-wised
Mixture of Gaussians distribution. The entire objective function for training
network is thus the combination of the supervised data loss (least square loss
between input clear image and the network output) and the unsupervised data
loss. In this way, all such unsupervised rainy images, which is much easier to
collect than supervised ones, can be rationally fed into the network training
process, and thus both the short-of-training-sample and
bias-to-supervised-sample issues can be evidently alleviated. Experiments
implemented on synthetic and real data experiments verify the superiority of
our model as compared to the state-of-the-arts.